A method for AI assisted human interpretation of neonatal EEG.

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method's suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually.

Towards Deeper Neural Networks for Neonatal Seizure Detection.

Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work proposes a deep neural network architecture which is capable of extracting information from long segments of EEG. Residual connections as well as data augmentation and a more robust optimizer are efficiently exploited to train a deeper architecture with an increased receptive field and longer EEG input.

An EEG analysis framework through AI and sonification on low power IoT edge devices.

This study explores the feasibility of implementation of an analysis framework of neonatal EEG, including ML, sonification and intuitive visualization, on a low power IoT edge device. Electroencephalography (EEG) analysis is a very important tool to detect brain disorders. Neonatal seizure detection is a known, challenging problem.

Deep Learning for EEG Seizure Detection in Preterm Infants.

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate.

Patient and Family Member Experiences in Critical Care Research and Quality Improvement Projects.

Background: Public and patient involvement in healthcare research is increasing, but the effect of involvement on individuals, service delivery, and health outcomes-particularly in specialist population groups like critical care-remains unclear, as does the best way to involve people who have experienced critical illness.

Objectives: The aim of the study was to explore former patients' and family members' views and experiences of involvement in critical care research and/or quality improvement.

Methods: Using a qualitative methodology, semistructured telephone interviews were conducted with seven former intensive care unit patients and three close family members across England.

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage.

Envisioning and shaping translation of knowledge into action: A comparative case-study of stakeholder engagement in the development of a European tobacco control tool.

Stakeholder engagement in health policy research is often said to increase 'research impact', but the active role of stakeholders in creating impact remains underexplored. We explored how stakeholders shaped the translation of health policy research into action. Our comparative case-study tracked a European research project that aimed to transfer an existing tobacco control return on investment tool.

A Hierarchy of Power: The Place of Patient and Public Involvement in Healthcare Service Development.

Amidst statutory and non-statutory calls for effective patient and public involvement (PPI), questions continue to be raised about the impact of PPI in healthcare services. Stakeholders, policy makers, researchers, and members of the public ask in what ways and at what level PPI makes a difference. Patient experience is widely seen as an important and valuable resource to the development of healthcare services, yet there remain legitimacy issues concerning different forms of knowledge that members of the public and professionals bring to the table, and related power struggles.

Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms.

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system.

How to engage stakeholders in research: design principles to support improvement.

Background: Closing the gap between research production and research use is a key challenge for the health research system. Stakeholder engagement is being increasingly promoted across the board by health research funding organisations, and indeed by many researchers themselves, as an important pathway to achieving impact. This opinion piece draws on a study of stakeholder engagement in research and a systematic literature search conducted as part of the study.

Whose voices? Patient and public involvement in clinical commissioning.

Aim: This paper aims to explore patient and public representation in a NHS clinical commissioning group and how this is experienced by staff and lay members involved.

Background: Patient and public involvement is believed to foster greater public representativeness in the development and delivery of health care services. However, there is widespread debate about what representation is or what it should be.